Automated segmentation of deep brain structures from Inversion-Recovery MRI

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Computational Neuroscience

Background:

• Automated segmentation of deep brain structures is challenging due to limited manual delineations.
• Accurate segmentation is crucial for understanding neurological disorders like Parkinson's disease.

Purpose of the Study:

• To assess automated segmentation of deep brain structures using White Matter Attenuated Inversion-Recovery (WAIR) MRI.
• To compare WAIR MRI with T1 and DTI imaging for segmenting five key structures in Parkinson's disease patients.

Main Methods:

• A state-of-the-art nnU-Net model was trained and tested on a clinical dataset of 53 Parkinson's patients.
• WAIR, T1, and DTI MRI images were used, along with manual segmentations of substantia nigra (SN), subthalamic nucleus (STN), red nucleus (RN), mammillary body (MB), and mammillothalamic fascicle (MT-fa).
• Evaluation metrics included Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (95HD), and Volumetric Similarity (VS). DTI vector reorientation was also assessed.

Main Results:

• WAIR MRI significantly outperformed T1 MRI across all metrics (DSC, 95HD, VS).
• Segmentation accuracy (DSC) decreased for MB, RN, SN, STN, and MT-fa, correlating with manual segmentation complexity.
• Reorienting DTI diffusion vectors improved automated segmentation performance.

Conclusions:

• WAIR MRI is a superior imaging modality for automated segmentation of deep brain structures compared to T1 MRI.
• The nnU-Net method demonstrates effective segmentation of complex deep brain structures, with performance varying by structure.
• DTI vector reorientation offers a potential enhancement for automated segmentation techniques.